Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization
Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by late...
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Main Authors | , , , , , , |
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Format | Journal Article |
Language | English |
Published |
18.11.2023
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Subjects | |
Online Access | Get full text |
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